Sensor localization technology is the principle problem for configuration and operation of wireless sensor network. Since existing multidimensional scaling localization algorithm has its limitation in localization accuracy, a novel method based on node distance correction (DCMDS) is put forward. The improved multidimensional scaling-based sensor localization algorithm partly divides the locatio...

Purpose – The purpose of this article is to investigate obesity-related queries from a public health portal (HealthLink) transaction log. Design/methodology/approach – Multidimensional scaling (MDS) was applied to each of five obesity-related focus keywords and their co-occurring terms in submitted queries. After the transaction log data were collected and cleaned, and query terms were extracte...

Previous algorithms for multidimensional scaling, or MDS, aim for scalable performance as the number of points to lay out increases. However, they either assume that the distance function is cheap to compute, and perform poorly when the distance function is costly, or they leave the precise number of distances to compute as a manual tuning parameter. We present Glint, an MDS algorithm framework...

Stock market indices SMIs are important measures of financial and economical performance. Considerable research efforts during the last years demonstrated that these signals have a chaotic nature and require sophisticated mathematical tools for analyzing their characteristics. Classical methods, such as the Fourier transform, reveal considerable limitations in discriminating different periods o...

Recommender systems are designed to assist individual users to navigate through the rapidly growing amount of information. One of the most successful recommendation techniques is the collaborative filtering, which has been extensively investigated and has already found wide applications in e-commerce. One of challenges in this algorithm is how to accurately quantify the similarities of user pai...

Dimension reduction techniques are widely used for the analysis and visualization of complex sets of data. This paper compares two nonlinear projection methods: Isomap and Curvilinear Distance Analysis. Contrarily to the traditional linear PCA, these methods work like multidimensional scaling, by reproducing in the projection space the pairwise distances measured in the data space. They differ ...

This paper analyzes the Portuguese short-run business cycles over the last 150 years and presents the multidimensional scaling (MDS) for visualizing the results. The analytical and numerical assessment of this long-run perspective reveals periods with close connections between the macroeconomic variables related to government accounts equilibrium, balance of payments equilibrium, and economic g...

Recently, a family of methods have been proposed for large scale ad hoc sensor networks that estimate the approximate positions of sensor nodes in the network. In the paper, we first investigate some situations that most existing sensor positioning methods fail to perform well. An example of such situations is that the topology of a sensor network is anisotropic. Then, we study the multidimensi...

Listeners judged the dissimilarity of pairs of synthesized nasal voices that varied on 3 dimensions. Separate nonmetric multidimensional scaling (MDS) solutions were calculated for each listener and the group. Similar 3-dimensional solutions were derived for the group and each of the listeners, with the group MDS solution accounting for 83% of the total variance in listeners' judgments. Dimensi...

Motivation Recent experiments have provided Hi-C data at resolution as high as 1 kbp. However, 3D structural inference from high-resolution Hi-C datasets is often computationally unfeasible using existing methods. Results We have developed miniMDS, an approximation of multidimensional scaling (MDS) that partitions a Hi-C dataset, performs high-resolution MDS separately on each partition, and ...